PTL 1 describes an example of an operation management system which models a system using time series information on system performance and determines a cause of a failure or an error of the system using the generated model.
The operation management system described in PTL 1 generates a correlation model of a system by determining a correlation function representing correlation for each pair of a plurality of metrics on the basis of measurement values of the plurality of metrics of the system. The operation management system detects destruction of the correlation (correlation destruction) using the generated correlation model, and determines a failure cause of the system on the basis of the correlation destruction. The technique analyzing a state of a system on the basis of correlation destruction in this way is called an invariant relation analysis. In the invariant relation analysis, for example, regarding a pair of metrics y and u, a correlation function for predicting the metric y on the basis of the metric u is used. Then, from a difference between an actual measurement value of the metric y in time series information at the time of model generation and a predicted value according to the correlation function, i.e. a prediction error, a permissible prediction error at the time of monitoring is calculated, and is set as a threshold value. A case in which a prediction error exceeds a threshold value at the time of monitoring is correlation destruction, and indicates occurrence of an error.
PTL 2, on the other hand, describes an example of a learning type process error diagnosis apparatus which models a system using time series information of an adjustable parameter, and determines a cause of a failure or an error and the like of the system using the generated model.
The learning type process error diagnosis apparatus described in PTL 2 generates a model from setting values of the adjustable parameter set multiple times in the past, and automatically calculates a possible normal range of the adjustable parameter on the basis of probability theory. The learning type process error diagnosis apparatus determines that the adjustable parameter has an error when the adjustable parameter goes beyond the calculated normal range.